To Adopt or Not to Adopt: Configurations for GenAI Recommendation Adoption in Sustainable Consumer Behavior

Abstract

Generative AI (GenAI) holds considerable promise for fostering sustainable consumer behavior, yet the mechanisms of trust-building and adoption remain underexplored. This study investigates how cognitive and motivational factors shape trust in GenAI-driven sustainability recommendations. Using fuzzy-set qualitative comparative analysis (fsQCA) on data from 577 participants in Indonesia, the findings show that high adoption arises from configurations of perceived information quality, relevance to sustainability, ease of implementation, and interaction quality. In contrast, low adoption is associated with a lack of trust and delicate perceptions of complexity and risk. The influence of perceived complexity varies across pathways, highlighting its contextual nature. Trust consistently stands out as a crucial condition for high adoption, underscoring its role in sustaining GenAI use. The study offers practical guidance for developers and policymakers, emphasizing the need to foster trust, streamline user interactions, and align GenAI solutions with broader sustainability goals. By addressing trust gaps and reducing complexity, GenAI can evolve into a transformative tool for advancing consumer-driven sustainable practices.